Assessing GHG emission trends using decomposition analysis

Ricardo Fernandez – European Environment AgencyCoordination of EU GHG Inventory (UNFCCC); analysis of GHG emission trends; international MRV

EIONET climate change mitigation

28 May 2019, Copenhagen

Outline of presentation

Objective: To give you an overview of the uses of decomposition analysis in GHG trend-analysis and to inform you about the EEA’s current and ongoing work

1. Background to EEA’s GHG-trend analysis

2. Short intro to decomposition-analysis methods

3. Pros and cons of the methods

4. Examples: annual, cumulative, period-comparison (% & CO2 eq.)

5. Ongoing and future work (ETC/CME task)

6. Conclusions

1. Background to EEA analysis of GHG emission trends

GHG inventories at the core of the MRV system but not enough to explain ‘why’ things happen

Is economic recession the only driver of GHG emission reductions during 2008-2012 (KP)?

EEA’s role to inform policy makers and the general public in parallel: technicalities vs simplicity

Increasing the usefulness from so much GHG reporting: numbers means something

Closing the circle: from MS reporting obligations to informing EU policy development (linking past trends and projections)

2. Methods: exploring the influence of key factors underpinning emission trends

IPAT [I = PAT]: Human Impact (I) on the environment equals the product of P= Population, A= Affluence, T= Technology

CO2 = population * GDP/population *CO2/GDP

KAYA: Total GHGs can be expressed as the product of four factors: population, GDP per capita, energy intensity, and carbon intensity

CO2 = population * GDP/population * energy/GDP * CO2/energy

Other extensions of the ‘technology’ IPAT factor (used by EEA)

CO2 energy = population * GDP/population * final energy/GDP (final energy intensity of GDP) * primary energy/final energy (energy efficiency) * fossil fuels/primary energy (renewables/nuclear) * CO2/fossil fuels (carbon intensity of fossil fuels)

Total GHG = population * GDP/population * primary energy/GDP (total energy intensity of GDP) * GHG energy/primary energy (carbon intensity of energy) * total GHG/energy GHG (non-energy sectors) [Examples 4.1 to 4.4 follow]

3. Pros and cons of decomposition-analysis methods

Advantages

Method used widely, also in scientific literature and IPCC reports Relatively easy to understand, implement and communicate The logic can be applied to any sector (with the right factors)

Disadvantates (or to be aware of)

The factors used in the identity have to be relevant (before use) The relationship between the variables is true by definition ~ no

variability Assumes independence between the variables Risk of overinterpreation of the results (keep it simple)

4.1 Example: annual decomposition applied to GHG emissions from energy (%)

Red line shows the actual change in total GHG emissions year-on-year

Stacked columns show the year-on-year effect of each factor in the ‘identiy’:

Positive impact on emissions in most years [lower GHGs]: Energy intensity of GDP Carbon intensity of energy

Negative impact on emissions in most years [higher GHGs]: Population GDP per capita

To explain the effect of each factor in one year compared to the previous year (%)

Lower GDP was the biggest factor in the reduction of emissions in 2009; but improved carbon intensity (renewables) helped reduced emissions even further

4.2 Example: cumulative, compared to a reference year (CO2 eq.)

Same data input as example 4.1, but answers a different question: what is the overall contribution of the different factors to the net GHG reduction (1383 million tonnes of CO2 eq.) between 1990 and 2014?

To explain the effect of the factors every year compared to a reference year (in million tonnes CO2 eq.)

4.3 Example: cumulative, comparing different periods (%)

Key findings:

1. [White lines]: GHG emissions decreased with increasing GDP in all periods (absolute decoupling), though emissions decreased faster during economic recession

2. [Red bars] Lower carbon intensity: a) fossil fuel switch from coal to gas for electricity and heat generation; and b) strong increase in renewable energy sources [in the context of lower nuclear production]

3. [Yellowish bars] Lower energy intensity of the economy: a) improvements in energy efficiency (transformation and end-use); and b) changes linked to the structure of the economy towards the services sector

Source: EEA report: Analysis of key trends and drivers in greenhouse gas emissions in the EU https://www.eea.europa.eu/publications/analysis‐of‐key‐trends‐and

After decomposition analysis one can/should dig deeper into the key factor/s of interest using additional sources, e.g. energy balances, HDDs, economic accounts; and/or using statistical methods (to test the factors statistically)

The economic recession could partly explain lower energy demand from industry/transport since 2008, but energy intensity also decreased in 2005-2008 where demand was high

4.4 Example: with projections & link to targets (need same parameters)

Key messages:

1. The same factors driving emission reductions in the past [red/yellow bars] are also expected to play a key role in the future, although to a different degree.

2. For EU, GHG emission reductions by 2030 (with existing MS’ PaMs) are consistent with a 30 % reduction compared with 1990 (including aviation).

3. More efforts to reduce GHG emissions will be needed to achieve the EU’s reduction target of at least 40 % by 2030.

4. Efforts should, together with lower energy intensity and higher efficiency, concentrate on further improving the carbon intensity of energy production and consumption.

5. There is also room to increase emission reduction efforts in non-energy sectors.

Source: EEA briefing https://www.eea.europa.eu/publications/trends‐and‐drivers‐in‐greenhouse

5. Ongoing work on sectoral decomposition analysis: ETC/CME task

ETC task includes: 4-page briefings (method, graphics, analysis), database & data-viewer

ETC/CME partners: NILU, UBA-V, RIVM and EMISIA

Link with other tasks: GHG and air pollutant emissions from the end-user perspective (ETC/CME partner: VITO) > adding value to the work produced by others

Draft list of sectors/identities (work starts in Q3/Q4): Total GHG emissions, excluding LULUCF CO2 emissions from energy combustion CO2 emissions from passenger cars CO2 emissions from freight transport CO2 emissions from heat and electricity production CO2 emissions from industry (end-user approach & including combustion and IPPU) CO2 emissions from the residential sector (end-user approach) CO2 emissions from the services sector (end-user approach) GHG emissions from agriculture (or split CH4 enteric fermentation & N2O soils) CO2 emissions from iron and steel (+ blast furnaces and coke ovens) HFC emissions from refrigeration and air conditioning

6. Conclusions

Decomposition analysis is a powerful tool that is simple to understand and easy to implement

Need to test the relevance of drivers ex-ante to avoid meaningless conclusions

Do not over-interpret, but use it as an exploratory tool that can be complemented with other methods

EEA will continue developing the work at sectoral level. The feedback by the Eionet will be very useful and will be considered in the development

Thank you for your attention! ricardo.fernandez@eea.europa.eu

European Environment Agencyhttp://www.eea.europa.eu/